What is a Neural Network?

Neural networks have several use cases across many industries, such as the following:

• Medical diagnosis by medical image classification
• Targeted marketing by social network filtering and behavioral data analysis
• Financial predictions by processing historical data of financial instruments
• Electrical load and energy demand forecasting
• Process and quality control
• Chemical compound identification

We give four of the important applications of neural networks below.

Computer vision

Computer vision is the ability of computers to extract information and insights from images and videos. With neural networks, computers can distinguish and recognize images similar to humans. Computer vision has several applications, such as the following:

• Visual recognition in self-driving cars so they can recognize road signs and other road users
• Content moderation to automatically remove unsafe or inappropriate content from image and video archives
• Facial recognition to identify faces and recognize attributes like open eyes, glasses, and facial hair
• Image labeling to identify brand logos, clothing, safety gear, and other image details

Speech recognition

Neural networks can analyze human speech despite varying speech patterns, pitch, tone, language, and accent. Virtual assistants like Amazon Alexa and automatic transcription software use speech recognition to do tasks like these:

• Assist call center agents and automatically classify calls
• Convert clinical conversations into documentation in real time
• Accurately subtitle videos and meeting recordings for wider content reach

Natural language processing

Natural language processing (NLP) is the ability to process natural, human-created text. Neural networks help computers gather insights and meaning from text data and documents. NLP has several use cases, including in these functions:

• Automated virtual agents and chatbots
• Automatic organization and classification of written data
• Business intelligence analysis of long-form documents like emails and forms
• Indexing of key phrases that indicate sentiment, like positive and negative comments on social media
• Document summarization and article generation for a given topic

Recommendation engines

Neural networks can track user activity to develop personalized recommendations. They can also analyze all user behavior and discover new products or services that interest a specific user. For example, Curalate, a Philadelphia-based startup, helps brands convert social media posts into sales. Brands use Curalate’s intelligent product tagging (IPT) service to automate the collection and curation of user-generated social content. IPT uses neural networks to automatically find and recommend products relevant to the user’s social media activity. Consumers don't have to hunt through online catalogs to find a specific product from a social media image. Instead, they can use Curalate’s auto product tagging to purchase the product with ease.

Artificial neural networks can be categorized by how the data flows from the input node to the output node. Below are some examples:

Feedforward neural networks

Feedforward neural networks process data in one direction, from the input node to the output node. Every node in one layer is connected to every node in the next layer. A feedforward network uses a feedback process to improve predictions over time.

Backpropagation algorithm

Artificial neural networks learn continuously by using corrective feedback loops to improve their predictive analytics. In simple terms, you can think of the data flowing from the input node to the output node through many different paths in the neural network. Only one path is the correct one that maps the input node to the correct output node. To find this path, the neural network uses a feedback loop, which works as follows:

1. Each node makes a guess about the next node in the path.
2. It checks if the guess was correct. Nodes assign higher weight values to paths that lead to more correct guesses and lower weight values to node paths that lead to incorrect guesses.
3. For the next data point, the nodes make a new prediction using the higher weight paths and then repeat Step 1.

Convolutional neural networks

The hidden layers in convolutional neural networks perform specific mathematical functions, like summarizing or filtering, called convolutions. They are very useful for image classification because they can extract relevant features from images that are useful for image recognition and classification. The new form is easier to process without losing features that are critical for making a good prediction. Each hidden layer extracts and processes different image features, like edges, color, and depth.

Artificial intelligence is the field of computer science that researches methods of giving machines the ability to perform tasks that require human intelligence. Machine learning is an artificial intelligence technique that gives computers access to very large datasets and teaches them to learn from this data. Machine learning software finds patterns in existing data and applies those patterns to new data to make intelligent decisions. Deep learning is a subset of machine learning that uses deep learning networks to process data.

Machine learning vs. deep learning

Traditional machine learning methods require human input for the machine learning software to work sufficiently well. A data scientist manually determines the set of relevant features that the software must analyze. This limits the software’s ability, which makes it tedious to create and manage.

On the other hand, in deep learning, the data scientist gives only raw data to the software. The deep learning network derives the features by itself and learns more independently. It can analyze unstructured datasets like text documents, identify which data attributes to prioritize, and solve more complex problems.

For example, if you were training a machine learning software to identify an image of a pet correctly, you would need to take these steps:

• Find and label thousands of pet images, like cats, dogs, horses, hamsters, parrots, and so on, manually.
• Tell the machine learning software what features to look for so it can identify the image using elimination. For instance, it might count the number of legs, then check for eye shape, ear shape, tail, fur, and so on.
• Manually assess and change the labeled datasets to improve the software’s accuracy. For example, if your training set has too many pictures of black cats, the software will correctly identify a black cat but not a white one.
• In deep learning, however, the neural networks would process all the images and automatically determine that they need to analyze the number of legs and the face shape first, then look at the tails last to correctly identify the animal in the image.